The present invention relates to a system and method for assessing the fraud risk of associated with a domain name.
Fraud is a deception deliberately practiced in order to secure unfair or unlawful gain (adjectival form fraudulent; to defraud is the verb). Fraud activity can happen in various forms, like the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them, for example by stealing personal information, which can even lead to identity theft. Internet services can be used to present fraudulent solicitations to prospective victims, to conduct fraudulent transactions, or to transmit the proceeds of fraud to financial institutions or to others connected with the scheme. Domain names can be created and used with fraudulent purposes, as a criminal deception intended to result in financial or personal gain.
The present invention seeks to determine the fraud risk of an Internet Domain Name by delivering critical information about the Internet domain name to help individuals and businesses minimize the risk of fraudulent domain names. The validation of an Internet domain name data is an important component when assessing the fraud risk associated with an Internet domain name, and data normalization. The solution is found in the present invention, which comprises a system and methodology for Internet domain name fraud risk assessment utilizing machine learning algorithms. The system and methodology involves the collection of data elements associated with an Internet domain name from numerous sources, categorizes the domain name, verifies the domain name's current and historical status, and verifies the existence of the Internet domain name, The system and methodology calculates and describes the fraud risk indicators for the Internet domain name.